How technology is destroying silos in the fashion value chain

This article first appeared in The state of fashion: technologyan in-depth report co-published by BoF and McKinsey & Company.

Rapidly changing consumer demand and ongoing supply chain disruptions are just a few of the factors that add to the complexity of operating a fashion brand today.

The industry needs a new digitized value chain model that unites multiple internal processes and data sources, from demand forecasting to pricing. In fact, when it comes to digitization, 61% of fashion executives believe that end-to-end process management is one of the most important areas of investment for their organizations between 2021 and 2025. The result will be businesses stronger, more resilient and capable of navigating today’s unstable business landscape.

Many fashion companies have improved individual value chain processes through digital technologies. But fully integrated backend systems and workflows are still a long way off.

State of Fashion Technology Report Table 8.

One reason is that relatively few off-the-shelf applications are designed to optimize the end-to-end fashion value chain. While companies like Nextail, Logility and O9 offer solutions that address certain activities such as purchasing, first product allocation, replenishment and store transfers, no single solution covers the entire supply chain. value. Brands must therefore identify solutions that address their pain points or create custom applications, which requires a lot of resources. At the same time, development costs remain high and companies face gaps in their technology stacks and talent pools.

Five critical workflow “paths” in the fashion value chain lend themselves to end-to-end integration: product performance, category performance, supply chain optimization, inventory management and purchasing forecasting and demand. Integrating key elements of a value chain journey could speed time to market by up to 50%, increase full-price sales by up to 8%, and reduce manufacturing costs by up to 20% .

State of Fashion Technology Report Table 11.

Product performance, or evaluating which products are selling well, shows the impact of end-to-end integration in practice. A siled pricing and promotions app can use AI and machine learning to determine a product’s promotional price by analyzing current stock, in-season price, in-season length, and expected elasticity . In contrast, investing in end-to-end integration would broaden the scope of the application to also take into account similar products already in store or coming soon, as well as expected returns or a competing range. Each of these data points impacts expected sales and therefore appropriate promotional pricing, which can ultimately increase gross margins.

At Levi’s, enterprise-wide machine learning combined with a cloud-based data repository of internal and external sales and inventory information empowers multiple processes to make better decisions across all areas, from pricing to consumer marketing, according to Katia, director of strategy and artificial intelligence. Walch. Data-driven knowledge sharing also helps Levi’s determine the best locations to ship products from, identifying the store or distribution center closest to the delivery address, helping to control costs. logistics and manage inventory smoothly.

Integrating key elements of a value chain journey could speed time to market by up to 50%.

Shein goes even further. Not only has the high-speed fashion player integrated its internal processes, but it has also linked these internal processes to those of its suppliers. This allows for a quick and efficient ordering and replenishment journey. Shein uses AI modeling to evaluate millions of social media posts across all platforms to determine which products to produce, while advanced analytics help its design teams examine the performance of design attributes up to details such as the zipper and the fabric. With its vertically integrated supply chain using Singbada’s software, Shein’s designs could reach customers within about three weeks of their first design.

Table 10 of the State of Fashion Technology Report.

Certainly, the operating models of fashion players will continue to require a fine balance between art and science so as not to lose sight of the creative and experience-driven aspects of decision-making that are essential in fashion. . Leaders must be prepared to deal with potential resistance to working in a more connected way, a way in which data and knowledge flow seamlessly through processes. Embracing deep digital integration will require a focus on change management. Teams will need to be honed or retrained, and tools will need to be designed with a user-centric mindset to ensure adoption. For example, this may mean adopting “explainable AI” in which AI predictions and results can be easily understood and managed by humans, unlike “black box” models which are difficult to interpret, and therefore reliable.

Ultimately, fashion companies – from mass market to luxury – will benefit from optimizing their time to market, flexibility and product availability at a time when many companies are struggling. to maintain their margins. Value chain integration will prove to be a critical point of competitive differentiation.

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